Current techniques for recommending new music to users of a recommending service tend to rely on active user participation. Users can purchase and rate music they like and, based on this information, a recommending service (e.g., a website) recommends new music for the user. For example, a user can indicate artists, genres, or songs as “favorites”. Other popular types of ratings include binary or numerical ratings, such that when a user listens to a song or album, they can rate whether or not they enjoyed the music, to what degree, and if it is a favorite. From these ratings, a recommending service can determine what songs, albums and artists the user may enjoy.
One drawback with this model of music recommendation is that the user must purchase or rate a significant amount of music before recommendations can be made, or before the recommendations become relevant to the user. Further, the user must continually update the ratings as music preferences change in order for the recommendations to continue to be relevant. Users may find this process frustrating as rating music can be tedious and time-consuming. For example, a user may forget to rate some favorite music or artists, resulting in recommendations that are irrelevant to the user. As a result, the user may abandon the recommending service as a resource for discovering new music.